000			02057cam a2200253 4500500
005			20260322003828.0
041			_afre
042			_adc
100	1	0	_aBachelot, Guillaume _eauthor
700	1	0	_aLy, Anna _eauthor
700	1	0	_aRivet-Danon, Diane _eauthor
700	1	0	_aSermondade, Nathalie _eauthor
700	1	0	_aFrydman, Valentine _eauthor
700	1	0	_aLamazière, Antonin _eauthor
700	1	0	_aHamid, Rahaf Haj _eauthor
700	1	0	_aLévy, Rachel _eauthor
700	1	0	_aDupont, Charlotte _eauthor
245	0	0	_aArtificial intelligence: Toward a better predictive strategy for testicular sperm extraction outcome in azoospermia
260			_c2024.
500			_a21
520			_aAzoospermia, defined as the absence of sperm in the semen, is found in 10–15% of infertile patients. Two-thirds of these cases are caused by impaired spermatogenesis, known as non-obstructive azoospermia (NOA). In this context, surgical sperm extraction using testicular sperm extraction (TESE) is the best option and can be offered to patients as part of fertility preservation, or for them to benefit from in vitro fertilization. The aim of the preoperative assessment is to identify the cause of NOA and evaluate the status of spermatogenesis. Its capacity to predict TESE success remains limited. As a result, no objective and reliable criteria are currently available to guide professionals on the chances of success and enable them to correctly assess the benefit–risk balance of this procedure. Artificial intelligence (AI), a field of research that has been rapidly expanding in recent years, has the potential to revolutionize medicine by making it more predictive and personalized. The aim of this review is to introduce AI and its key concepts, and then to examine the current state of research into predicting the success of TESE.
786	0		_nAnnales de Biologie Clinique | 82 | 2 | 2024-03-01 | p. 139-149 | 0003-3898
856	4	1	_uhttps://stm.cairn.info/journal-annales-de-biologie-clinique-2024-2-page-139?lang=en&redirect-ssocas=7080
999			_c1735342 _d1735342